Using Wireless Sensor Networks to Achieve Intelligent Monitoring for High-Temperature Gas-Cooled Reactor by Jianghai Li, Jia Meng, Xiaojing Kang, Zhenhai Long, Xiaojin Huang

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Beyond raw data: AI-driven biosensor fusion for enhancing athletic performance by Peng Luo, Jun Song

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Detection of terrain feature points from digital elevation models using contour context by Jiapei Hu, Xuejun Liu, Bo Wu

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Optimization of IoT-Based Motion Intelligence Monitoring System by Jian Qiao, Zhendong Zhang, Enqing Chen

“…The system provides a multiuser-oriented communication method and realizes accurate and reliable human movement monitoring, which has a wide application prospect in the fields of physical education and rehabilitation training.…”
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A novel data representation framework based on nonnegative manifold regularisation by Yan Jiang, Wei Liang, Jintian Tang, Hongbo Zhou, Kuan-Ching Li, Jean-Luc Gaudiot

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Artificial intelligence, generative artificial intelligence and research integrity: a hybrid systemic review by Khalid H. Arar, Hamit Özen, Gülşah Polat, Selahattin Turan

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A novel motion key frame extraction and video stream classification based on reinforcement learning and feature fusion by Hongbo Cui, Tao Feng, Jinhui Zheng

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Research on the evolution of college online public opinion risk based on improved Grey Wolf Optimizer combined with LSTM model. by Chao Cao, Ziyu Li, Lingzhi Li, Fanglu Luo

“…This research proposes a public opinion crisis prediction model that applies the Grey Wolf Optimizer (GWO) algorithm combined with long short-term memory (LSTM) and implements it to analyze a trending topic on Sina Weibo to validate its prediction accuracy. …”
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Neural network based active control of base isolated structure considering isolator nonlinearity by Nour Elhouda Ghanemi, Mahdi Abdeddaim, Abdelhafid Ounis, Michela Basili

“…The ANN-driven controller aims to achieve significant response reduction with fewer sensors than traditional algorithms while enhancing robustness against signal time delays and white noise contamination. …”
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Enhancing Regional Topsoil Total Nitrogen Mapping Through Differentiated Fusion of Ground Hyperspectral Data and Satellite Images Under Low Vegetation Cover by Rongpeng He, Jihua Meng, Yanfei Du, Zhenxin Lin, Xinyan You, Xinyu Gao

“…Therefore, a differentiated fusion of enhanced multispectral image bands (DFE_MSIBs) method combined with Random Forest (RF) algorithms was developed for spectral inversion of STN content. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes. • The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. …”
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A Study on the Management and Evolution of Land Use and Land Cover in Romania During the Period 1990–2022 in the Context of Political and Environmental Changes by Jenica Călina, Aurel Călina, Gheorghe Marian Vangu, Alin Constantin Croitoru, Marius Miluț, Nicolae Ion Băbucă, Ion Stan

“…Land use and land cover are the main anthropogenic factors that lead to the rapid and aggressive degradation of land and interfere with the functioning of ecosystems, especially through the expansion of urbanization and the reduction in forested areas. The purpose of this article is to identify sources of official data and to build an updated dataset upon which analysis algorithms can be applied. …”
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Optimization and benefit evaluation model of a cloud computing-based platform for power enterprises by Xueli Yin, Xuguang Zhang, Luyao Pei, Rong Hu, Kaihui Ye, Kangkang Cai

“…In addition, through containerized deployment and intelligent orchestration, it achieves a 43% reduction in monthly operating costs. A multi-level benefit evaluation system—spanning power generation, grid operations, and end-user services—is established, integrating historical data, expert weighting, and dynamic optimization algorithms to enable quantitative performance assessment and decision support. …”
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Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images by Lili Zhang, Zihan Jin, Yibo Wang, Ziyi Wang, Zeyu Duan, Taoran Qi, Rui Shi

“…Furthermore, it attained a sub-10% cross-sectional calculation error for hollowing dimensions, outperforming maximum entropy (70.5% error reduction) and OTSU (7.4% error reduction) methods, which shows our method being one novel method for automated intelligent hollowing detection.…”
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The role of artificial intelligence in breast cancer screening as a supportive tool for radiologists by Agata Król, Katarzyna Kwaterska, Karol Kutyłowski, Paweł Łuckiewicz

“…Early diagnosis is crucial for cancer-related burden and mortality reduction. For this reason several countries have implemented breast cancer screening programme. …”
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Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images by G. Savitha, S. Girisha, Pundarika Sughosh, Dasharathraj K. Shetty, Jayaraj Mymbilly Balakrishnan, Rahul Paul, Nithesh Naik

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EDWNet: A Novel Encoder–Decoder Architecture Network for Water Body Extraction from Optical Images by Tianyi Zhang, Wenbo Ji, Weibin Li, Chenhao Qin, Tianhao Wang, Yi Ren, Yuan Fang, Zhixiong Han, Licheng Jiao

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Semantic influence of programming on the development of thinking of students: background, research and prospects by M. U. Mukasheva, Y. V. Payevskaya

“…Altynsarin with the support of the grant from the Ministry of Education and Science of the Republic of Kazakhstan. …”
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Vehicle routing problem in waste collection: a case study in Son Tra, Da Nang by Le Hoang Son, Truong Nguyen Song Ha, Nguyen Ngoc Phuong, Phan Bao An

“…By employing the Dijkstra algorithm, route optimization resulted in a significant reduction in average route length, from 12.94 ± 3.5 km to 9.42 ± 2.67 km, representing a 9-42% improvement. …”
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On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data by Mohamad Abed El Rahman Hammoud, Nikolaos Papagiannopoulos, George Krokos, Robert J. W. Brewin, Dionysios E. Raitsos, Omar Knio, Ibrahim Hoteit

“…Our results suggest that BNNs perform at least as well as established methods, and they could achieve 20–40% lower mean squared errors when additional input variables are included, such as the sea surface temperature and its climatological mean alongside the coordinates of the prediction. The BNNs offer means for uncertainty quantification by estimating the probability distribution of [CHL-a], building confidence in the [CHL-a] predictions through the variance of the predictions. …”
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